Content
- Patterns of drug resistance in space and time
- Rural and regional areas
- Local rates
The goal of HOTspots is to display the data about antimmicrobial resistance in the Top End in a way that is easy to understand and useable for the audience. The audience nicludes members of the public, medical professionals and policy/decision makers.
Other options that might be important or interesting - age - social disadvantage - remoteness index - household structure - gender - international areas
The data is supplied by the HOTspots team; currently the member that supplies the data is Will Cunningham. The data is saved to a dropbox folder which is shared with Alys Young. The data is comprised of 2 csv files; HOTspots_yearly_age&sex.csv and HOTspots_monthly.csv. The HOTspots_yearly_age&sex.csv file contains annual antimicrobial resistance data split for each age and sex category. The HOTspots_monthly.csv file contains monthly resistance data.
## The raw data
## Monthly
hotspot_data <- read.csv("www/data/HOTspots_monthly.csv")
hotspot_data$date_dmy <- as.Date(paste("01", hotspot_data$month_year), format = "%d %b %y")
head(hotspot_data)
#> source jurisdiction region sample_type onset organism
#> 1 Human FNQ Cairns and Hinterland All Community A. baumannii
#> 2 Human FNQ Cairns and Hinterland All Community A. baumannii
#> 3 Human FNQ Cairns and Hinterland All Community A. baumannii
#> 4 Human FNQ Cairns and Hinterland All Community A. baumannii
#> 5 Human FNQ Cairns and Hinterland All Community A. baumannii
#> 6 Human FNQ Cairns and Hinterland All Community A. baumannii
#> antimicrobial year month_year num_of_tests_monthly_raw resistant_monthly_raw
#> 1 Amikacin 2008 Mar 08 1 0
#> 2 Amikacin 2009 May 09 1 0
#> 3 Amikacin 2010 May 10 1 0
#> 4 Amikacin 2010 Aug 10 1 0
#> 5 Amikacin 2010 Sep 10 1 0
#> 6 Amikacin 2010 Nov 10 1 0
#> susceptible_monthly_raw percent_resistant_monthly_raw date_dmy
#> 1 1 0 2008-03-01
#> 2 1 0 2009-05-01
#> 3 1 0 2010-05-01
#> 4 1 0 2010-08-01
#> 5 1 0 2010-09-01
#> 6 1 0 2010-11-01
## Yearly
hotspot_yearly_data_full <- read.csv("www/data/HOTspots_yearly_age&sex.csv")
head(hotspot_yearly_data_full)
#> source jurisdiction region sample_type onset organism
#> 1 Human FNQ Cairns and Hinterland All Community A. baumannii
#> 2 Human FNQ Cairns and Hinterland All Community A. baumannii
#> 3 Human FNQ Cairns and Hinterland All Community A. baumannii
#> 4 Human FNQ Cairns and Hinterland All Community A. baumannii
#> 5 Human FNQ Cairns and Hinterland All Community A. baumannii
#> 6 Human FNQ Cairns and Hinterland All Community A. baumannii
#> antimicrobial year age_group sex num_of_tests_yearly_raw resistant_yearly_raw
#> 1 Amikacin 2008 61-80 F 1 0
#> 2 Amikacin 2009 26-40 F 1 0
#> 3 Amikacin 2010 6-15 F 1 0
#> 4 Amikacin 2010 16-25 F 1 0
#> 5 Amikacin 2010 26-40 F 1 0
#> 6 Amikacin 2010 41-60 F 1 0
#> susceptible_yearly_raw percent_resistant_yearly_raw num_of_tests_yearly
#> 1 1 0 NA
#> 2 1 0 NA
#> 3 1 0 NA
#> 4 1 0 NA
#> 5 1 0 NA
#> 6 1 0 NA
#> resistant_yearly susceptible_yearly percent_resistant_yearly
#> 1 NA NA NA
#> 2 NA NA NA
#> 3 NA NA NA
#> 4 NA NA NA
#> 5 NA NA NA
#> 6 NA NA NAThe HOTspots_yearly_age&sex.csv file is cleaned in the Data_manipulation.R script to create 4 files, 3 of which are loaded for the shiny app. Cleaning includes removing NAs and duplicates, and aggregating across superfluous groups.
hotspot_yearly_data <- read.csv("www/data/hotspot_yearly_data.csv")
head(hotspot_yearly_data, n= 2)
#> source jurisdiction region sample_type onset organism
#> 1 Human FNQ Cairns and Hinterland All Community E. coli
#> 2 Human FNQ Cairns and Hinterland All Community E. coli
#> antimicrobial year age_group sex percent_resistant_yearly_overall
#> 1 Amikacin 2008 Overall Both 0
#> 2 Amikacin 2009 Overall Both 0
#> percent_susceptible_yearly_overall num_of_tests_yearly_overall
#> 1 100 123
#> 2 100 106
#> num_of_resistant_tests_yearly_overall num_of_susceptible_tests_yearly_overall
#> 1 0 123
#> 2 0 106
hotspot_yearly_splitage <- read.csv("www/data/hotspot_yearly_splitage.csv")
# Note: The column "Age" shows age brackets and the column "sex" has the value 'both' for all records
head(hotspot_yearly_splitage, n= 2)
#> source jurisdiction region sample_type onset organism
#> 1 Human FNQ Cairns and Hinterland All Community E. coli
#> 2 Human FNQ Cairns and Hinterland All Community E. coli
#> antimicrobial year age_group sex percent_resistant_yearly_overall
#> 1 Amikacin 2008 16-25 Both 0
#> 2 Amikacin 2008 26-40 Both 0
#> percent_susceptible_yearly_overall num_of_tests_yearly_overall
#> 1 100 29
#> 2 100 39
#> num_of_resistant_tests_yearly_overall num_of_susceptible_tests_yearly_overall
#> 1 0 29
#> 2 0 39
hotspot_yearly_splitsex <- read.csv("www/data/hotspot_yearly_splitsex.csv")
# Note: The column "Age" shows 'overall' for all records and the column "sex" has the value F for female sex or M for male sex
head(hotspot_yearly_splitsex, n= 2)
#> source jurisdiction region sample_type onset organism
#> 1 Human FNQ Cairns and Hinterland All Community E. coli
#> 2 Human FNQ Cairns and Hinterland All Community E. coli
#> antimicrobial year age_group sex percent_resistant_yearly_overall
#> 1 Amikacin 2008 Overall F 0
#> 2 Amikacin 2009 Overall F 0
#> percent_susceptible_yearly_overall num_of_tests_yearly_overall
#> 1 100 110
#> 2 100 97
#> num_of_resistant_tests_yearly_overall num_of_susceptible_tests_yearly_overall
#> 1 0 110
#> 2 0 97The regions that antimicrobial resistance is mapped across are displayed on the map below.
#> OGR data source with driver: ESRI Shapefile
#> Source: "/Users/alys/Documents/2. RA Hotspots/HOTspots/www/data/Australian_regions/Aus_regions.shp", layer: "Aus_regions"
#> with 316 features
#> It has 4 fields